SIVOCS

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# SIVOCS

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## B1: How familiar are you with the concept of “social innovation”

**H**: The familiarity with the concept of SI depends on the field of research.

---
# B1: Is SI familiarity normally distributed?

.pull-left[

```
## 
## 	Shapiro-Wilk normality test
## 
## data: data$familiarWithSI.response.
## W = 0.87186, p-value < 2.2e-16
```
* H_o = Normally distributed ==> SI familiarity is not normally distributed
]

.pull-right[

]

---
# B1 (OLD) : ANOVA test: SI familiarity depends on sci. domains?

.small[ Caution, we are assuming familiarity with SI has an interval scale and variance homogenity]

```
##              Df Sum Sq Mean Sq F value   Pr(>F)    
## domain        2  451.4  225.70   28.76 2.62e-12 ***
## Residuals   357 2801.5    7.85                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingness
```

```
## # A tibble: 3 × 3
## group1 group2 p.value
## <chr> <chr> <dbl>
## 1 Humanities and Social Sciences Biology and Medicine 6.22e-11
## 2 Math., Natur. and Eng. Sci. Biology and Medicine 2.65e- 1
## 3 Math., Natur. and Eng. Sci. Humanities and Social Sciences 8.26e- 9
```

---
# B1 (NEW): Kruskal-Wallis test: SI familiarity depends on sci. domains?

```
## 
## 	Bartlett test of homogeneity of variances
## 
## data:  familiarWithSI.response. by domain
## Bartlett's K-squared = 12.931, df = 2, p-value = 0.001556
```

```
## 
## 	Kruskal-Wallis rank sum test
## 
## data:  familiarWithSI.response. by domain
## Kruskal-Wallis chi-squared = 45.694, df = 2, p-value = 1.196e-10
```

* Mean ranks of the groups are not the same, SI fam. depends on domain

---

# B1: Wilcox test: SI familiarity depends on sci. domains?

.pull-left[
* SSH vs other

```
## 
## 	Wilcoxon rank sum test with continuity correction
## 
## data:  familiarWithSI.response. by SSH
## W = 8590, p-value = 2.001e-11
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -3.000023 -1.999999
## sample estimates:
## difference in location 
##              -2.000026
```

]

.pull-right[
* NS vs other

```
## 
## 	Wilcoxon rank sum test with continuity correction
## 
## data:  familiarWithSI.response. by Math._Natur._and_Eng.
## W = 17358, p-value = 0.003771
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.0000604398 1.0000123644
## sample estimates:
## difference in location 
##              0.9999553
```
]

---

# B1: *Familiarity with SI* across scientific domains

* *Familiarity with SI* differs across scientific domains (ANOVA test: p &lt; 0.05)
* *Biology and Medicine* and *Math., Natur, and Eng. Sci.* are similar (pairwise t-test: p &gt; 0.05)
* *Humanities and Social Sciences* are significantly different than the others (pairwise t-test with each: p &lt; 0.05)

---

class: clear, center

**H**: Familiarity with SI correlates with familiarity with transdisciplinarity

---

# C2: Correlation with SI familiarity

.pull-left[

```
## 
## 	Spearman's rank correlation rho
## 
## data:  b1domain.df$trans_exp and b1domain.df$familiarWithSI
## S = 4454962, p-value = 4.957e-14
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.3871266
```

]

.pull-right[

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]

---
class:clear, center
# A2

**H**: Transdisciplinary research depends on age

---
# A2: Fam. with Trans. normal distributed?

```
## 
## 	Shapiro-Wilk normality test
## 
## data:  data$transdisciplinaryExp.rate.
## W = 0.94158, p-value = 1.487e-10
```
* Reject H_o = A2 normal distributed

---
# A2: Transdisciplinary research depends on age ?

* Homogeneity of variances

```
## 
## 	Bartlett test of homogeneity of variances
## 
## data:  transdisciplinaryExp.rate. by age
## Bartlett's K-squared = 1.5345, df = 4, p-value = 0.8205
```

```
## 
## 	Kruskal-Wallis rank sum test
## 
## data:  transdisciplinaryExp.rate. by age
## Kruskal-Wallis chi-squared = 2.4474, df = 4, p-value = 0.6541
```
* Can't reject the H_o = there is no stat. significant difference between age groups in terms of transdisciplinary experience

---
class:clear 
# D1: Motivation types

H: Motivation to improve the human condition/welfare (D1.c) correlates with contribution to better services/products for general population.

.pull-left[

```
## 
## 	Spearman's rank correlation rho
## 
## data: data.questions$motivation.welfare. and data.questions$impactTargetGroup.pub.
## S = 3282110, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho 
## 0.5406923
```
]

.pull-right[

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---
class:clear
##  H:  Generating deeper/better understanding of a specific social issue depends on (the level of) transdisciplinary involvement of citizens

```
## 
## 	Shapiro-Wilk normality test
## 
## data: data.questions$Impactstatements.understanding.
## W = 0.81298, p-value < 2.2e-16
```

```
## 
## 	Shapiro-Wilk normality test
## 
## data: data.questions$groupsInvolved.citiz.
## W = 0.5649, p-value < 2.2e-16
```

---

```
## 
## 	Kruskal-Wallis rank sum test
## 
## data:  Impactstatements.understanding. by groupsInvolved.citiz.
## Kruskal-Wallis chi-squared = 44.018, df = 2, p-value = 2.765e-10
```
* Different involvement levels make difference.

---

```
## 
## 	Spearman's rank correlation rho
## 
## data:  data.questions$Impactstatements.understanding. and data.questions$groupsInvolved.citiz.
## S = 2207337, p-value = 1.404e-11
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.3901644
```

---

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---

# D1: Motivation Types

## H: Motivation Types depend on sci. domains

---
###### Phenomenon

.pull-left[
<img src="data:image/png;base64,#16_survey_tests_files/figure-html/unnamed-chunk-23-1.svg" width="504" />
]

.pull-right[

```
## 
## 	Shapiro-Wilk normality test
## 
## data: data.questions$motivation.pheno.
## W = 0.65462, p-value < 2.2e-16
```

```
## 
## 	Kruskal-Wallis rank sum test
## 
## data:  motivation.pheno. by domain
## Kruskal-Wallis chi-squared = 0.037102, df = 2, p-value = 0.9816
```
* No stat. sig. difference between domains

]

---

###### Problem

.pull-left[

]

.pull-right[

```
## 
## 	Shapiro-Wilk normality test
## 
## data: data.questions$motivation.prob.
## W = 0.87279, p-value < 2.2e-16
```

```
## 
## 	Kruskal-Wallis rank sum test
## 
## data:  motivation.prob. by domain
## Kruskal-Wallis chi-squared = 8.3372, df = 2, p-value = 0.01547
```
* Stat. sig. difference between domains

]
---

###### Welfare

.pull-left[
<img src="data:image/png;base64,#16_survey_tests_files/figure-html/unnamed-chunk-29-1.svg" width="504" />
]

.pull-right[

```
## 
## 	Shapiro-Wilk normality test
## 
## data:  data.questions$motivation.welfare.
## W = 0.91547, p-value = 3.131e-13
```

```
## 
## 	Kruskal-Wallis rank sum test
## 
## data:  motivation.welfare. by domain
## Kruskal-Wallis chi-squared = 31.592, df = 2, p-value = 1.38e-07
```
* Stat. sig. difference between domains

]